Advances in neural information processing systems 2
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple graph matching with Bayesian inference
Pattern Recognition Letters - special issue on pattern recognition in practice V
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Computation of Error-Correcting Graph Isomorphisms Based on Model Precompilation
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A Structural and Semantic Probabilistic Model for Matching and Representing a Set of Graphs
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
On the Computation of the Common Labelling of a Set of Attributed Graphs
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Constellations and the unsupervised learning of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Graph database retrieval based on metric-trees
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Component retrieval based on a database of graphs for Hand-Written Electronic-Scheme Digitalisation
Expert Systems with Applications: An International Journal
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The computation of a common labelling of a set of graphs is required to find a representative of a given graph set. Although this is a NP-problem, practical methods exist to obtain a sub-optimal common labelling in polynomial time. We consider the graphs in the set have a Gaussian distortion, and so, the average labelling is the one that obtains the best common labelling. In this paper, we present two new algorithms to find a common labelling between a set of attributed graphs, which are based on a probabilistic framework. They have two main advantages. From the theoretical point of view, no additional nodes are artificial introduced to obtain the common labelling, and so, the structure of the graphs in the set is kept unaltered. From the practical point of view, results show that the presented algorithms outperform state-of-the-art algorithms.